We rely on experts all the time. If you need financial advice, you ask an expert. If you are sick, you visit a doctor, and as a juror you may listen to an expert witness. In the future, however, artificial intelligence (AI) might replace many of these people. In forensic science, the expert witness plays a vital role.

We rely on experts all the time. If you need financial advice, you ask an expert. If you are sick, you visit a doctor, and as a juror you may listen to an expert witness. In the future, however, artificial intelligence (AI) might replace many of these people. In forensic science, the expert witness plays a vital role.

Entity synonyms discovery is crucial for entity-leveraging applications. However, existing studies suffer from several critical issues: (1) the input mentions may be out-of-vocabulary (OOV) and may come from a different semantic space of the entities; (2) the connection between mentions and entities may be hidden and cannot be established by surface matching; and (3) some entities rarely appear due to the long-tail effect. To tackle these challenges, we facilitate knowledge graphs and propose a novel entity synonyms discovery framework, named \emph{KGSynNet}. Specifically, we pre-train subword embeddings for mentions and entities using a large-scale domain-specific corpus while learning the knowledge embeddings of entities via a joint TransC-TransE model. More importantly, to obtain a comprehensive representation of entities, we employ a specifically designed \emph{fusion gate} to adaptively absorb the entities' knowledge information into their semantic features. We conduct extensive experiments to demonstrate the effectiveness of our \emph{KGSynNet} in leveraging the knowledge graph. The experimental results show that the \emph{KGSynNet} improves the state-of-the-art methods by 14.7\% in terms of hits@3 in the offline evaluation and outperforms the BERT model by 8.3\% in the positive feedback rate of an online A/B test on the entity linking module of a question answering system.

I am a law professor who teaches torts and has been studying driverless cars for almost a decade. Despite the headlines, I am reasonably convinced U.S. common law is going to adapt to driverless cars just fine. The courts have seen hundreds of years of new technology, including robots. American judges have had to decide, for example, whether a salvage operation exercises exclusive possession over a shipwreck by visiting it with a robot submarine (it does) and whether a robot copy of a person can violate their rights of publicity (it can). Assigning liability in the event of a driverless car crash is not, in the run of things, all that tall an order.

We suggest to employ propositional satisfiability techniques in solving a problem of cooperative multi-robot path-finding optimally. Several propositional encodings of path-finding problems have been suggested recently. In this paper we evaluate how efficient these encodings are in solving certain cases of cooperative path-findings problems optimally. Particularly, a case where robots have multiple optional locations as their targets is considered in this paper.